Artificial Intelligence and data: the revolution in the supply chain

Artificial intelligence is transforming companies, with supply chain processes being one of the areas that is obtaining the greatest benefit. Its management involves all resource management activities, including the acquisition of materials, manufacturing, storage and transportation from origin to final destination.

In recent years, business systems have been modernized and are now supported by increasingly ubiquitous computer networks. Within these networks, sensors, machines, systems, vehicles, smart devices and people are interconnected and continuously generating information. To this must be added the increase in computational capacity, which allows us to process these large amounts of data generated quickly and efficiently. All these advances have contributed to stimulating the application of Artificial Intelligence technologies that offer a sea of ​​possibilities.

In this article we are going to review some Artificial Intelligence applications at different points in the supply chain.

Technological implementations in the different phases of the supply chain

Planning

According Gartner, volatility in demand is one of the aspects that most concern entrepreneurs. The COVID-19 crisis has highlighted the weakness in planning capacity within the supply chain. In order to properly organize production, it is necessary to know the needs of the customers. This can be done through techniques of predictive analytics that allow us to predict demand, that is, estimate a probable future request for a product or service. This process also serves as the starting point for many other activities, such as warehousing, shipping, product pricing, purchasing raw materials, production planning, and other processes that aim to meet demand.

Access to real-time data allows the development of Artificial Intelligence models that take advantage of all the contextual information to obtain more precise results, reducing the error significantly compared to more traditional forecasting methods such as ARIMA or exponential smoothing.

Production planning is also a recurring problem where variables of various kinds play an important role. Artificial intelligence systems can handle information involving material resources; the availability of human resources (taking into account shifts, vacations, leave or assignments to other projects) and their skills; the available machines and their maintenance and information on the manufacturing process and its dependencies to optimize production planning in order to satisfactorily meet the objectives.

Production

Within of the stages of the production process, one of the stages more driven by the application of artificial intelligence is the quality control and, more specifically, the detection of defects. According to European Comission, 50% of the production can end up as scrap due to defects, while, in complex manufacturing lines, the percentage can rise to 90%. On the other hand, non-automated quality control is an expensive process, as people need to be trained to be able to perform the inspections properly and, furthermore, these manual inspections could cause bottlenecks in the production line, delaying delivery times. Coupled with this, inspectors do not increase in number as production increases.

In this scenario, the application of computer vision algorithms can solve all these problems. These systems learn from defect examples and can thus extract common patterns to be able to classify future production defects. The advantages of these systems is that they can achieve the precision of a human or even better, since they can process thousands of images in a very short time and are scalable.

On the other hand, it is very important to ensure the reliability of the machinery and reduce the chances of production stoppage due to breakdowns. In this sense, many companies are betting on predictive maintenance systems that are capable of analyzing monitoring data to assess the condition of the machinery and schedule maintenance if necessary.

Open data can help when training these algorithms. As an example, the Nasa offers a collection of data sets donated by various universities, agencies or companies useful for the development of prediction algorithms. These are mostly time series of data from a normal operating state to a failed state. This article shows how one of these specific data sets (Turbofan Engine Degradation Simulation Data Set, which includes sensor data from 100 engines of the same model) can be taken to perform a exploratory analysis and a model of linear regression reference.

Transport

Route optimization is one of the most critical elements in transportation planning and business logistics in general. Optimal planning ensures that the load arrives on time, reducing cost and energy to a minimum. There are many variables that intervene in the process, such as work peaks, traffic incidents, weather conditions, etc. And that's where artificial intelligence comes into play. A route optimizer based on artificial intelligence is able to combine all this information to offer the best possible route or modify it in real time depending on the incidents that occur during the journey.

Logistics organizations use transport data and official maps to optimize routes in all modes of transport, avoiding areas with high congestion, improving efficiency and safety. According to the study “Open Data impact Map”, The open data most demanded by these companies are those directly related to the means of transport (routes, public transport schedules, number of accidents…), but also geospatial data, which allow them to better plan their trips.

In addition, exist companies that share their data in B2B models. As stated in the Cotec Foundation report “Guide for opening and sharing data in the business environment”, The Spanish company Primafrio, shares data with its customers as an element of value in their operations for the location and positioning of the fleet and products (real-time data that can be useful to the customer, such as the truck license plate, position, driver , etc.) and for billing or accounting tasks. As a result, your customers have optimized order tracking and their ability to advance billing.

Closing the transport section, uOne of the objectives of companies in the logistics sector is to ensure that goods reach their destination in optimal conditions. This is especially critical when working with companies in the food industry. Therefore, it is necessary to monitor the state of the cargo during transport. Controlling variables such as temperature, location or detecting impacts is crucial to know how and when the load deteriorated and, thus, be able to take the necessary corrective actions to avoid future problems. Technologies such as IoT, Blockchain and Artificial Intelligence are already being applied to these types of solutions, sometimes including the use of open data.

Customer service

Offering good customer service is essential for any company. The implementation of conversational assistants allows to enrich the customer experience. These assistants allow users to interact with computer applications conversationally, through text, graphics or voice. By means of speech recognition techniques and natural language processing, these systems are capable of interpreting the intention of users and taking the necessary actions to respond to their requests. In this way, users could interact with the wizard to track their shipment, modify or place an order. In the training of these conversational assistants it is necessary to use quality data, to achieve an optimal result.

In this article we have seen only some of the applications of artificial intelligence to different phases of the supply chain, but its capacity is not only limited to these. There are other applications such as automated storage used by Amazon at its facilities, dynamic prices depending on the demand or the application of artificial intelligence in marketing, which only give an idea of ​​how artificial intelligence is revolutionizing consumption and society.

Content elaborated by Jose Antonio Sanchez, expert in Data Science and enthusiast of the Artificial Intelligence.

Contents and points of view expressed in this publication are the exclusive responsibility of its author.